The demand by navigation systems, such as the navigation systems utilized by manned vehicles as well as the navigation systems utilized by autonomous self-driving vehicles, for high-definition maps is ever increasing. In order to generate high-definition maps, a high resolution digital terrain model (DTM) or a digital elevation model (DEM), which provides two-dimensional (2D) discrete functions of elevation, must generally be constructed. For purposes of the subsequent discussion, both a DTM and a DEM will generally be referenced as a DEM. A DEM defines the elevation of the terrain without consideration of the structures, such as buildings, or other objects, such as trees, that rise above and occlude the terrain.
To survey a large area, most DEM providers, such as the U.S. Geological Survey (USGS) and the Earth Remote Sensing Data Analysis Center (ERDAC) employ flying platforms, such as remote sensing satellites and airborne laser scanners. These acquisition techniques enable a large territory to be surveyed, but the data captured by the flying platforms has relatively low precision and introduces measurement errors, such that the resolution of the resulting DEM is on the level of meters. In contrast, the resolution of high-definition maps, such as those desired for navigation systems of autonomous self-driving cars, is much smaller, such as millimeter-level in some instances. Additionally, surveying an area from a flying platform is limited by various constraints, such as cost and operating altitude, such that system error and random error are difficult to reduce. Further, the data captured by a survey conducted from a flying platform initially defines a digital surface model (DSM), which includes buildings, trees and other objects which, in turn, can distort the representation of the underlying terrain that is essential for navigation purposes. A DEM can be computed from a DSM, but the computation comes with significant additional effort. In this regard, a DEM may be computed from the DSM in an automated fashion or manual effort may be employed to remove the objects occluding the terrain. For example, the DSM obtained from a flying platform may be measured, verified and/or corrected by manual survey that defines various control points throughout the area surveyed from the flying platform. However, the manual survey requires significant manpower and resources and can only provide coverage of a relatively small area.
Since the survey of an area from a flying platform utilizing, for example, LiDAR cannot penetrate through obstacles that cover the underlying terrain, such as trees in a forest, the use of ground-based LiDAR to generate a DTM has been proposed. At least some of these techniques rely upon sensors that are fixed upon the ground and, as a result, may only survey relatively small regions. Additionally, the techniques that utilize ground-based LiDAR also utilize other methodologies to remove non-surface objects which, in turn, increase the computing resources and/or the manpower required to generate a DEM. Additionally, these techniques are generally limited to the removal of non-surface objects, such as buildings and trees, that are much smaller than the surrounding surface area that is not occluded and that is directly measured by ground-based LiDAR. As such, the use of ground-based LiDAR is limited and, in at least some instances, is impractical since the non-surface objects are frequently larger objects, such as buildings or the like, which cannot be effectively removed in order to generate the DEM.